Overview

Dataset statistics

Number of variables16
Number of observations4258
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory532.4 KiB
Average record size in memory128.0 B

Variable types

Numeric15
Boolean1

Alerts

Clay is highly correlated with Sand and 2 other fieldsHigh correlation
Sand is highly correlated with Clay and 1 other fieldsHigh correlation
Silt is highly correlated with Sand and 1 other fieldsHigh correlation
pH(CaCl2) is highly correlated with Clay and 2 other fieldsHigh correlation
pH(H2O) is highly correlated with Clay and 2 other fieldsHigh correlation
EC is highly correlated with NHigh correlation
OC is highly correlated with NHigh correlation
CaCO3 is highly correlated with pH(CaCl2) and 1 other fieldsHigh correlation
N is highly correlated with Silt and 2 other fieldsHigh correlation
Clay is highly correlated with Sand and 2 other fieldsHigh correlation
Sand is highly correlated with Clay and 1 other fieldsHigh correlation
Silt is highly correlated with SandHigh correlation
pH(CaCl2) is highly correlated with Clay and 2 other fieldsHigh correlation
pH(H2O) is highly correlated with Clay and 2 other fieldsHigh correlation
EC is highly correlated with NHigh correlation
OC is highly correlated with NHigh correlation
CaCO3 is highly correlated with pH(CaCl2) and 1 other fieldsHigh correlation
N is highly correlated with EC and 1 other fieldsHigh correlation
Clay is highly correlated with SandHigh correlation
Sand is highly correlated with Clay and 1 other fieldsHigh correlation
Silt is highly correlated with SandHigh correlation
pH(CaCl2) is highly correlated with pH(H2O) and 1 other fieldsHigh correlation
pH(H2O) is highly correlated with pH(CaCl2) and 1 other fieldsHigh correlation
OC is highly correlated with NHigh correlation
CaCO3 is highly correlated with pH(CaCl2) and 1 other fieldsHigh correlation
N is highly correlated with OCHigh correlation
df_index is highly correlated with Point_ID and 1 other fieldsHigh correlation
Point_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Revisited_point is highly correlated with df_index and 1 other fieldsHigh correlation
Clay is highly correlated with Sand and 3 other fieldsHigh correlation
Sand is highly correlated with Clay and 1 other fieldsHigh correlation
Silt is highly correlated with Clay and 1 other fieldsHigh correlation
pH(CaCl2) is highly correlated with Clay and 2 other fieldsHigh correlation
pH(H2O) is highly correlated with Clay and 2 other fieldsHigh correlation
EC is highly correlated with N and 1 other fieldsHigh correlation
OC is highly correlated with NHigh correlation
CaCO3 is highly correlated with pH(CaCl2) and 1 other fieldsHigh correlation
N is highly correlated with EC and 1 other fieldsHigh correlation
K is highly correlated with ECHigh correlation
df_index has unique values Unique
Point_ID has unique values Unique
Coarse has 45 (1.1%) zeros Zeros
CaCO3 has 1364 (32.0%) zeros Zeros
P has 675 (15.9%) zeros Zeros

Reproduction

Analysis started2022-06-07 03:41:56.856217
Analysis finished2022-06-07 03:42:54.473579
Duration57.62 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4258
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2222.00822
Minimum4
Maximum21025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:54.707171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile216.85
Q11070.25
median2137.5
Q33201.75
95-th percentile4057.15
Maximum21025
Range21021
Interquartile range (IQR)2131.5

Descriptive statistics

Standard deviation1797.120631
Coefficient of variation (CV)0.8087821707
Kurtosis54.06963899
Mean2222.00822
Median Absolute Deviation (MAD)1066
Skewness5.394471118
Sum9461311
Variance3229642.564
MonotonicityStrictly increasing
2022-06-07T09:12:54.956680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
33951
 
< 0.1%
33671
 
< 0.1%
13221
 
< 0.1%
33711
 
< 0.1%
13261
 
< 0.1%
33751
 
< 0.1%
13301
 
< 0.1%
33791
 
< 0.1%
13341
 
< 0.1%
Other values (4248)4248
99.8%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
210251
< 0.1%
210241
< 0.1%
210231
< 0.1%
210221
< 0.1%
210211
< 0.1%
210201
< 0.1%
210191
< 0.1%
210181
< 0.1%
210171
< 0.1%
210161
< 0.1%

Point_ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4258
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40562988.21
Minimum28061794
Maximum64981672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:55.225405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum28061794
5-th percentile30002319.3
Q132862168
median39762407
Q346862647
95-th percentile54601857.4
Maximum64981672
Range36919878
Interquartile range (IQR)14000479

Descriptive statistics

Standard deviation8455944.61
Coefficient of variation (CV)0.2084645383
Kurtosis-0.579222749
Mean40562988.21
Median Absolute Deviation (MAD)6960509
Skewness0.5222537656
Sum1.727172038 × 1011
Variance7.150299924 × 1013
MonotonicityNot monotonic
2022-06-07T09:12:55.488916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410624021
 
< 0.1%
314423221
 
< 0.1%
380823721
 
< 0.1%
328021701
 
< 0.1%
344037081
 
< 0.1%
315221141
 
< 0.1%
386430701
 
< 0.1%
324622081
 
< 0.1%
496449301
 
< 0.1%
410433341
 
< 0.1%
Other values (4248)4248
99.8%
ValueCountFrequency (%)
280617941
< 0.1%
281022761
< 0.1%
281422801
< 0.1%
281818741
< 0.1%
281822821
< 0.1%
282017861
< 0.1%
282018181
< 0.1%
282021701
< 0.1%
282218881
< 0.1%
282619041
< 0.1%
ValueCountFrequency (%)
649816721
< 0.1%
649616761
< 0.1%
649016721
< 0.1%
649016681
< 0.1%
648816661
< 0.1%
648416701
< 0.1%
648416661
< 0.1%
648216681
< 0.1%
648016681
< 0.1%
646616601
< 0.1%

Revisited_point
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
4233 
True
 
25
ValueCountFrequency (%)
False4233
99.4%
True25
 
0.6%
2022-06-07T09:12:55.823208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Coarse
Real number (ℝ≥0)

ZEROS

Distinct87
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.63386566
Minimum0
Maximum90
Zeros45
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:55.992539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median19
Q330
95-th percentile49
Maximum90
Range90
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.80410171
Coefficient of variation (CV)0.6843021926
Kurtosis1.025218181
Mean21.63386566
Median Absolute Deviation (MAD)10
Skewness0.9487540972
Sum92117
Variance219.1614274
MonotonicityNot monotonic
2022-06-07T09:12:56.226906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11137
 
3.2%
17131
 
3.1%
23129
 
3.0%
12128
 
3.0%
19126
 
3.0%
20123
 
2.9%
15121
 
2.8%
4120
 
2.8%
14118
 
2.8%
18117
 
2.7%
Other values (77)3008
70.6%
ValueCountFrequency (%)
045
 
1.1%
191
2.1%
284
2.0%
3112
2.6%
4120
2.8%
5112
2.6%
694
2.2%
791
2.1%
897
2.3%
9109
2.6%
ValueCountFrequency (%)
901
 
< 0.1%
862
 
< 0.1%
841
 
< 0.1%
833
0.1%
821
 
< 0.1%
811
 
< 0.1%
801
 
< 0.1%
792
 
< 0.1%
781
 
< 0.1%
775
0.1%

Clay
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.20854861
Minimum0
Maximum62
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:56.505653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median18
Q326
95-th percentile39
Maximum62
Range62
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.72489841
Coefficient of variation (CV)0.5583398632
Kurtosis0.1905720927
Mean19.20854861
Median Absolute Deviation (MAD)7.5
Skewness0.7247110128
Sum81790
Variance115.0234458
MonotonicityNot monotonic
2022-06-07T09:12:56.751539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9182
 
4.3%
11168
 
3.9%
10167
 
3.9%
12154
 
3.6%
8153
 
3.6%
14151
 
3.5%
21150
 
3.5%
13150
 
3.5%
6150
 
3.5%
18150
 
3.5%
Other values (52)2683
63.0%
ValueCountFrequency (%)
02
 
< 0.1%
112
 
0.3%
224
 
0.6%
350
 
1.2%
478
1.8%
5102
2.4%
6150
3.5%
7122
2.9%
8153
3.6%
9182
4.3%
ValueCountFrequency (%)
621
 
< 0.1%
601
 
< 0.1%
591
 
< 0.1%
582
 
< 0.1%
574
0.1%
565
0.1%
553
0.1%
542
 
< 0.1%
533
0.1%
527
0.2%

Sand
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.41874119
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:57.029900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q122
median34
Q350
95-th percentile74
Maximum100
Range98
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.17138548
Coefficient of variation (CV)0.512347152
Kurtosis-0.3704194592
Mean37.41874119
Median Absolute Deviation (MAD)13
Skewness0.5732820738
Sum159329
Variance367.5420212
MonotonicityNot monotonic
2022-06-07T09:12:57.297938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33111
 
2.6%
20101
 
2.4%
2998
 
2.3%
2195
 
2.2%
2395
 
2.2%
2294
 
2.2%
1991
 
2.1%
2689
 
2.1%
2788
 
2.1%
2887
 
2.0%
Other values (85)3309
77.7%
ValueCountFrequency (%)
22
 
< 0.1%
31
 
< 0.1%
45
 
0.1%
58
 
0.2%
620
 
0.5%
711
 
0.3%
832
0.8%
939
0.9%
1043
1.0%
1155
1.3%
ValueCountFrequency (%)
1002
 
< 0.1%
971
 
< 0.1%
962
 
< 0.1%
941
 
< 0.1%
935
0.1%
924
0.1%
912
 
< 0.1%
903
 
0.1%
885
0.1%
878
0.2%

Silt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.36683889
Minimum0
Maximum72
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:57.563459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q135
median45
Q353
95-th percentile61
Maximum72
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.5456273
Coefficient of variation (CV)0.289290795
Kurtosis-0.1798274759
Mean43.36683889
Median Absolute Deviation (MAD)8
Skewness-0.5164259259
Sum184656
Variance157.3927643
MonotonicityNot monotonic
2022-06-07T09:12:57.803868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52144
 
3.4%
44141
 
3.3%
49139
 
3.3%
50138
 
3.2%
46135
 
3.2%
53135
 
3.2%
48132
 
3.1%
47132
 
3.1%
51132
 
3.1%
45130
 
3.1%
Other values (62)2900
68.1%
ValueCountFrequency (%)
02
 
< 0.1%
21
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
54
0.1%
63
 
0.1%
73
 
0.1%
82
 
< 0.1%
95
0.1%
108
0.2%
ValueCountFrequency (%)
721
 
< 0.1%
713
 
0.1%
702
 
< 0.1%
694
 
0.1%
687
 
0.2%
6714
 
0.3%
6619
0.4%
6525
0.6%
6432
0.8%
6340
0.9%

pH(CaCl2)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.822475341
Minimum2.8
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:58.082721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.7
Q14.5
median5.9
Q37.2
95-th percentile7.6
Maximum8.5
Range5.7
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.368199789
Coefficient of variation (CV)0.2349859311
Kurtosis-1.389999614
Mean5.822475341
Median Absolute Deviation (MAD)1.3
Skewness-0.1952792444
Sum24792.1
Variance1.871970662
MonotonicityNot monotonic
2022-06-07T09:12:58.320489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4234
 
5.5%
7.5230
 
5.4%
7.3201
 
4.7%
7.2195
 
4.6%
7.6170
 
4.0%
7.1165
 
3.9%
7136
 
3.2%
4.4124
 
2.9%
4.1118
 
2.8%
4.2118
 
2.8%
Other values (44)2567
60.3%
ValueCountFrequency (%)
2.82
 
< 0.1%
36
 
0.1%
3.110
 
0.2%
3.222
 
0.5%
3.323
 
0.5%
3.437
0.9%
3.539
0.9%
3.643
1.0%
3.768
1.6%
3.880
1.9%
ValueCountFrequency (%)
8.51
 
< 0.1%
8.11
 
< 0.1%
81
 
< 0.1%
7.94
 
0.1%
7.817
 
0.4%
7.759
 
1.4%
7.6170
4.0%
7.5230
5.4%
7.4234
5.5%
7.3201
4.7%

pH(H2O)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct485
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1819845
Minimum3.47
Maximum9.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:58.559282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.47
5-th percentile4.1185
Q14.95
median6.16
Q37.51
95-th percentile8.03
Maximum9.05
Range5.58
Interquartile range (IQR)2.56

Descriptive statistics

Standard deviation1.353989184
Coefficient of variation (CV)0.2190217695
Kurtosis-1.366943265
Mean6.1819845
Median Absolute Deviation (MAD)1.29
Skewness-0.09573515043
Sum26322.89
Variance1.833286712
MonotonicityNot monotonic
2022-06-07T09:12:58.811692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9527
 
0.6%
7.8527
 
0.6%
7.825
 
0.6%
825
 
0.6%
7.625
 
0.6%
7.9324
 
0.6%
7.8823
 
0.5%
4.6921
 
0.5%
7.8920
 
0.5%
7.7520
 
0.5%
Other values (475)4021
94.4%
ValueCountFrequency (%)
3.471
 
< 0.1%
3.531
 
< 0.1%
3.541
 
< 0.1%
3.563
0.1%
3.591
 
< 0.1%
3.61
 
< 0.1%
3.611
 
< 0.1%
3.621
 
< 0.1%
3.652
< 0.1%
3.664
0.1%
ValueCountFrequency (%)
9.051
< 0.1%
8.911
< 0.1%
8.691
< 0.1%
8.561
< 0.1%
8.551
< 0.1%
8.471
< 0.1%
8.451
< 0.1%
8.441
< 0.1%
8.421
< 0.1%
8.41
< 0.1%

EC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1973
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.73511977
Minimum1.73
Maximum599.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:59.095473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile4.2485
Q111.015
median17.775
Q332.1
95-th percentile85.075
Maximum599.6
Range597.87
Interquartile range (IQR)21.085

Descriptive statistics

Standard deviation32.80522114
Coefficient of variation (CV)1.18280438
Kurtosis42.65886011
Mean27.73511977
Median Absolute Deviation (MAD)8.86
Skewness4.792975001
Sum118096.14
Variance1076.182534
MonotonicityNot monotonic
2022-06-07T09:12:59.343865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2116
 
0.4%
20.216
 
0.4%
20.412
 
0.3%
24.811
 
0.3%
24.311
 
0.3%
20.811
 
0.3%
26.411
 
0.3%
20.910
 
0.2%
23.510
 
0.2%
20.610
 
0.2%
Other values (1963)4140
97.2%
ValueCountFrequency (%)
1.731
< 0.1%
2.092
< 0.1%
2.121
< 0.1%
2.131
< 0.1%
2.141
< 0.1%
2.151
< 0.1%
2.161
< 0.1%
2.191
< 0.1%
2.261
< 0.1%
2.281
< 0.1%
ValueCountFrequency (%)
599.61
< 0.1%
4051
< 0.1%
3741
< 0.1%
3401
< 0.1%
3271
< 0.1%
3081
< 0.1%
2771
< 0.1%
2641
< 0.1%
2621
< 0.1%
2591
< 0.1%

OC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1198
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.20246595
Minimum0.4
Maximum517.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:12:59.597371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile6.5
Q115.2
median29.1
Q354
95-th percentile137.615
Maximum517.2
Range516.8
Interquartile range (IQR)38.8

Descriptive statistics

Standard deviation59.05191066
Coefficient of variation (CV)1.278111665
Kurtosis21.95284535
Mean46.20246595
Median Absolute Deviation (MAD)16.5
Skewness4.085733597
Sum196730.1
Variance3487.128152
MonotonicityNot monotonic
2022-06-07T09:12:59.865421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.816
 
0.4%
20.516
 
0.4%
11.716
 
0.4%
1616
 
0.4%
15.215
 
0.4%
15.315
 
0.4%
11.114
 
0.3%
11.614
 
0.3%
14.914
 
0.3%
10.814
 
0.3%
Other values (1188)4108
96.5%
ValueCountFrequency (%)
0.41
 
< 0.1%
0.72
< 0.1%
0.81
 
< 0.1%
0.92
< 0.1%
12
< 0.1%
1.11
 
< 0.1%
1.22
< 0.1%
1.61
 
< 0.1%
1.91
 
< 0.1%
2.14
0.1%
ValueCountFrequency (%)
517.21
< 0.1%
513.61
< 0.1%
506.71
< 0.1%
5061
< 0.1%
502.21
< 0.1%
497.31
< 0.1%
494.71
< 0.1%
494.11
< 0.1%
493.71
< 0.1%
490.51
< 0.1%

CaCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct594
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.6209488
Minimum0
Maximum907
Zeros1364
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:13:00.273686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q381.75
95-th percentile504
Maximum907
Range907
Interquartile range (IQR)81.75

Descriptive statistics

Standard deviation169.1137744
Coefficient of variation (CV)1.952342669
Kurtosis4.294532975
Mean86.6209488
Median Absolute Deviation (MAD)1
Skewness2.226698018
Sum368832
Variance28599.46869
MonotonicityNot monotonic
2022-06-07T09:13:00.519774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01364
32.0%
11041
24.4%
2115
 
2.7%
388
 
2.1%
441
 
1.0%
531
 
0.7%
724
 
0.6%
624
 
0.6%
820
 
0.5%
5519
 
0.4%
Other values (584)1491
35.0%
ValueCountFrequency (%)
01364
32.0%
11041
24.4%
2115
 
2.7%
388
 
2.1%
441
 
1.0%
531
 
0.7%
624
 
0.6%
724
 
0.6%
820
 
0.5%
97
 
0.2%
ValueCountFrequency (%)
9071
< 0.1%
8981
< 0.1%
8781
< 0.1%
8461
< 0.1%
8161
< 0.1%
8151
< 0.1%
7941
< 0.1%
7892
< 0.1%
7881
< 0.1%
7831
< 0.1%

P
Real number (ℝ≥0)

ZEROS

Distinct782
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.76383279
Minimum0
Maximum1017.6
Zeros675
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:13:00.762624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.3
median12
Q326.8
95-th percentile74.845
Maximum1017.6
Range1017.6
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation31.97482945
Coefficient of variation (CV)1.469172722
Kurtosis233.4925381
Mean21.76383279
Median Absolute Deviation (MAD)8.3
Skewness9.54078445
Sum92670.4
Variance1022.389718
MonotonicityNot monotonic
2022-06-07T09:13:01.030963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0675
 
15.9%
7.134
 
0.8%
6.833
 
0.8%
6.732
 
0.8%
9.730
 
0.7%
8.530
 
0.7%
7.330
 
0.7%
6.229
 
0.7%
8.928
 
0.7%
5.627
 
0.6%
Other values (772)3310
77.7%
ValueCountFrequency (%)
0675
15.9%
2.32
 
< 0.1%
2.53
 
0.1%
2.62
 
< 0.1%
2.84
 
0.1%
2.91
 
< 0.1%
31
 
< 0.1%
3.17
 
0.2%
3.21
 
< 0.1%
3.44
 
0.1%
ValueCountFrequency (%)
1017.61
< 0.1%
380.61
< 0.1%
315.71
< 0.1%
309.21
< 0.1%
262.41
< 0.1%
256.11
< 0.1%
247.51
< 0.1%
242.11
< 0.1%
235.61
< 0.1%
232.31
< 0.1%

N
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct199
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.495302959
Minimum0.1
Maximum32.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:13:01.281189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.7
Q11.4
median2.5
Q34.3
95-th percentile9.7
Maximum32.3
Range32.2
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation3.374013435
Coefficient of variation (CV)0.9652992814
Kurtosis13.00077819
Mean3.495302959
Median Absolute Deviation (MAD)1.3
Skewness3.016819269
Sum14883
Variance11.38396666
MonotonicityNot monotonic
2022-06-07T09:13:01.531183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3145
 
3.4%
1.2144
 
3.4%
1.1137
 
3.2%
1.6127
 
3.0%
1.4114
 
2.7%
1.5112
 
2.6%
1104
 
2.4%
1.7101
 
2.4%
0.8101
 
2.4%
2.499
 
2.3%
Other values (189)3074
72.2%
ValueCountFrequency (%)
0.16
 
0.1%
0.25
 
0.1%
0.312
 
0.3%
0.427
 
0.6%
0.568
1.6%
0.665
1.5%
0.787
2.0%
0.8101
2.4%
0.986
2.0%
1104
2.4%
ValueCountFrequency (%)
32.31
< 0.1%
28.91
< 0.1%
28.11
< 0.1%
281
< 0.1%
27.31
< 0.1%
26.91
< 0.1%
26.31
< 0.1%
26.11
< 0.1%
25.31
< 0.1%
25.21
< 0.1%

K
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2659
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.2023955
Minimum4.5
Maximum9873.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.4 KiB
2022-06-07T09:13:01.813349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile31.1
Q181
median146.95
Q3253.775
95-th percentile530.79
Maximum9873.7
Range9869.2
Interquartile range (IQR)172.775

Descriptive statistics

Standard deviation240.5725484
Coefficient of variation (CV)1.189761119
Kurtosis627.5753774
Mean202.2023955
Median Absolute Deviation (MAD)78.75
Skewness17.21083731
Sum860977.8
Variance57875.15103
MonotonicityNot monotonic
2022-06-07T09:13:02.069715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.68
 
0.2%
49.87
 
0.2%
55.17
 
0.2%
161.47
 
0.2%
78.26
 
0.1%
766
 
0.1%
56.46
 
0.1%
84.26
 
0.1%
62.96
 
0.1%
94.26
 
0.1%
Other values (2649)4193
98.5%
ValueCountFrequency (%)
4.51
< 0.1%
5.21
< 0.1%
5.61
< 0.1%
6.21
< 0.1%
71
< 0.1%
7.91
< 0.1%
82
< 0.1%
8.11
< 0.1%
9.81
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
9873.71
< 0.1%
3674.11
< 0.1%
1938.51
< 0.1%
1868.31
< 0.1%
1834.31
< 0.1%
1823.31
< 0.1%
1773.11
< 0.1%
1772.21
< 0.1%
1502.41
< 0.1%
1498.41
< 0.1%

Elevation
Real number (ℝ)

Distinct1364
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824.5162048
Minimum-30
Maximum11890
Zeros10
Zeros (%)0.2%
Negative28
Negative (%)0.7%
Memory size33.4 KiB
2022-06-07T09:13:02.318910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile62
Q1359.5
median1016.5
Q31163.75
95-th percentile1413
Maximum11890
Range11920
Interquartile range (IQR)804.25

Descriptive statistics

Standard deviation514.6675164
Coefficient of variation (CV)0.6242054594
Kurtosis87.4971512
Mean824.5162048
Median Absolute Deviation (MAD)279.5
Skewness4.010219735
Sum3510790
Variance264882.6524
MonotonicityNot monotonic
2022-06-07T09:13:02.584936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104815
 
0.4%
104115
 
0.4%
105015
 
0.4%
103614
 
0.3%
100914
 
0.3%
108014
 
0.3%
103213
 
0.3%
106013
 
0.3%
112113
 
0.3%
107013
 
0.3%
Other values (1354)4119
96.7%
ValueCountFrequency (%)
-301
 
< 0.1%
-171
 
< 0.1%
-161
 
< 0.1%
-141
 
< 0.1%
-122
< 0.1%
-111
 
< 0.1%
-101
 
< 0.1%
-93
0.1%
-82
< 0.1%
-71
 
< 0.1%
ValueCountFrequency (%)
118901
< 0.1%
111911
< 0.1%
40201
< 0.1%
23961
< 0.1%
21101
< 0.1%
20241
< 0.1%
19391
< 0.1%
18561
< 0.1%
18361
< 0.1%
18331
< 0.1%

Interactions

2022-06-07T09:12:50.006031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:01.594336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:05.053846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:08.665308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:12.191916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:15.766658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:19.277694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:22.709333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:26.221799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:29.542507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:33.063681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:36.519186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:39.766488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:43.206305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:46.553324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:50.229331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:01.836761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:05.286702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:08.887484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:12.416883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:15.987449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:19.497494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:22.923015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:26.432013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:29.910627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:33.278267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:36.733718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:39.978007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:43.421665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:46.764699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:50.475923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:02.089670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:05.539662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:09.124119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:12.659589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:16.231573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:19.744242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:23.309329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:26.669393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:30.160038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:33.514868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:36.971307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:40.216707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:43.661686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:47.002204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:50.691346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:02.297099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:05.769443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:09.342183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:12.885445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:16.453437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:19.964936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:23.522818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:26.884802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:30.372687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:33.723770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:37.178513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:40.426128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:43.873804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:47.209912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:50.925845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:02.517790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:06.007022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:09.569791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:13.156322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:16.833821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:20.195860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:23.747075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:27.111213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:30.602470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:33.943419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:37.402467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:40.647742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:44.101546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:47.438441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:51.153806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:02.732737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:06.249012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:09.928689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:13.384336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:17.054551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:20.421624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:23.969152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:27.336077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:30.823167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:34.163721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:37.607542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:40.862831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:44.315614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:47.657060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:51.393247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:03.054663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:06.503740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:10.174919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:13.636675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:17.295402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:20.661574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:24.205749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:27.568379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:31.057840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:34.400727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:37.843562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:41.093456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:44.549400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:47.888387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:51.624351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:03.284936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:06.737320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:10.405775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:13.876576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:17.523720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:20.887676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:24.421365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:27.789067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:31.279330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:34.620488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:38.055013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:41.310468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:44.767890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:48.108838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:51.854773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:03.497790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:06.970541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:10.632327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:14.121555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:17.747432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:21.114287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:24.644062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:28.007190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:31.501640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:34.843870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:38.272700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:41.528667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:44.995511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:48.324449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:52.095349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:03.718931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:07.216158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:10.856740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:14.361406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:17.970432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:21.352065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:24.868244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:28.231300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:31.729831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:35.065842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:38.486308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:41.756671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:45.223202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:48.547527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:52.322431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:03.937126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:07.451769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:11.080603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:14.603744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:18.192993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:21.574905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:25.103543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:28.448074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:31.950816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:35.282997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:38.694489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:41.970665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:45.452677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:48.761515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:52.541303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:04.145998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:07.673095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:11.293472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:14.827799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:18.399045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:21.788054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:25.338870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:28.652075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:32.168071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:35.490333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:38.904987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:42.174588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:45.665711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:48.971771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:52.767715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:04.364042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:07.910453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:11.518332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:15.056657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:18.610791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:22.016678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:25.552924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:28.868040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:32.385248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:35.708363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:39.115414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:42.387763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:45.881437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:49.189374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:52.995641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:04.598901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:08.150627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:11.740333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:15.298510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:18.833639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:22.248638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:25.772544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:29.095547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:32.613183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:35.929996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:39.331404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:42.612120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:46.101777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:49.547221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:53.228005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:04.814996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:08.399471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:11.960793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:15.525808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:19.047617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:22.472844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:25.990766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:29.313180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:32.830614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:36.147635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:39.544959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:42.980462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:46.324996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-06-07T09:12:49.777076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-06-07T09:13:02.846051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-07T09:13:03.204667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-07T09:13:03.562548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-07T09:13:03.935415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-07T09:12:53.684367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-07T09:12:54.272079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexPoint_IDRevisited_pointCoarseClaySandSiltpH(CaCl2)pH(H2O)ECOCCaCO3PNKElevation
0434463934No28.010.046.044.03.94.0428.4043.116.32.338.6315
1533983238No18.014.036.050.04.24.4141.8032.407.53.348.0137
2634043240No20.018.035.046.04.95.1332.0021.1112.42.136.0131
3733723266No13.014.036.050.04.04.1672.4053.2052.14.2158.5137
4834203268No34.019.048.034.03.73.8711.6316.013.71.024.4514
5933663268No28.08.071.020.04.03.9922.2016.018.31.130.0232
61034123260No26.013.039.048.04.74.9435.0046.5049.73.6153.1377
71133723292No27.022.015.063.04.64.7981.1040.3075.55.296.2152
81234163274No28.04.079.017.03.03.7344.00506.0084.120.5197.2570
91333523262No23.025.017.058.04.14.3352.9048.2014.64.6103.6168

Last rows

df_indexPoint_IDRevisited_pointCoarseClaySandSiltpH(CaCl2)pH(H2O)ECOCCaCO3PNKElevation
42482101664621644Yes13.040.017.043.07.58.0018.7511.325255.81.6921.142
42492101764541646Yes13.029.024.047.07.37.8822.1024.360878.32.0775.9216
42502101863941630Yes21.016.048.036.06.77.105.613.500.00.4102.81028
42512101964981672Yes34.040.018.042.07.67.9815.1718.11800.02.2558.2107
42522102064121612Yes29.028.028.044.07.57.9416.1813.25700.02.2373.1327
42532102164841666Yes6.023.055.022.07.07.4431.306.4296.70.9589.439
42542102264841670Yes17.048.021.030.07.48.0219.5411.21763.41.5835.652
42552102364161658Yes15.033.034.032.07.68.1019.125.8856.50.7337.1240
42562102463921638Yes23.019.046.035.07.07.5010.345.210.00.556.2707
42572102564421628Yes14.039.021.040.07.47.8618.435.076514.31.5358.5161